1. Field of the Invention
This invention relates to computer-aided detection of clustered microcalcifications from digital mammograms, and particularly to techniques for decreasing the number of false positive detections without diminishing the number of true positives detected.
2. Discussion of the Background
As part of an ongoing effort to assist radiologists in interpreting mammograms, various computer-aided-diagnosis CAD schemes for detecting clustered microcalcifications or breast masses on mammograms have been reported in the literature; namely see
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The theory is that by alerting radiologists to suspicious regions on a mammogram, the accuracy of mammography can be increased. The computer could be used as a "second opinion" by radiologists, so that in effect, each mammogram could be double read. This is important because, with several medical associations advocating periodic mammographic screening, mammography may become one of the most common types of radiographs interpreted by radiologists.
In all or most of these techniques, signals that are smaller than some predetermined size are eliminated as false signals based on the assumption that very small signals in the image are caused by noise. However, the techniques implemented have required excessive processing capability to achieve reasonably accurate detections.
One important step in a CAD system is to group or cluster microcalcifications, since clustered microcalcifications are more clinically significant than are isolated microcalcifications. In fact, clustered microcalcifications are an important early indicator of malignancy and are sometimes the only indication of breast cancer visible in the mammogram. The presence of individual (non-clustered) microcalcifications are in most cases not clinically significant. Therefore, it is more important that a computer detection scheme find clustered as opposed to individual microcalcifications. Prior techniques for finding clustered microcalcifications have met with moderate, but nevertheless limited success.
Additionally, while prior research has investigated various feature analysis techniques for the removal of false signals from mammograms, even further improvement in the number of false positive clusters detected is desired.